The Gemma Threat: How Google Could Upend OAI and Anthropic

The Gemma Threat: How Google Could Upend OAI and Anthropic #

Google releasing a 120B dense multimodal Gemma model would be a game-changer, and I’m not just saying that because u/LLaMA_Lover_22 in the r/LocalLLaMA thread claimed it’s “the nuclear option” for taking down OpenAI and Anthropic. With a model of this size, Google would essentially be throwing its massive resources at the problem, potentially leaving competitors in the dust. This is overkill for most people, but for those who need the absolute best performance, a 120B model would be incredibly tempting. I mean, who wouldn’t want a model that can handle complex, multimodal inputs with ease? As one commenter pointed out, the current 13B LLaMA model is already a beast, requiring 24GB of RAM to run smoothly. A 120B model would need significantly more, likely in the range of 128-256GB of RAM, which is just ridiculous for most use cases.

The Multimodal Advantage #

What really sets a 120B dense multimodal Gemma model apart, though, is its ability to handle multiple types of input simultaneously. We’re talking text, images, audio - you name it. This is a huge advantage over current models, which often struggle with more than one type of input. For example, the recent LLaMA v2 model has made significant strides in handling text-based inputs, but it still falters when faced with more complex, multimodal tasks. A 120B Gemma model would likely leave it in the dust. That being said, I haven’t tested this on ARM, and I’m not sure how well it would perform on lower-end hardware. The community is genuinely split on this, with some claiming that ARM-based systems are the future of AI, while others argue that they just can’t compete with the raw power of x86. Your mileage may vary, I suppose.

Competitor Comparison #

So, how would a 120B dense multimodal Gemma model stack up against the competition? Well, as one commenter pointed out, Anthropic’s recent Claude model has been making waves with its impressive performance on complex tasks. However, at 1.5B parameters, it’s still significantly smaller than what Google is proposing. OpenAI’s latest models, on the other hand, have been criticized for being overly focused on text-based inputs, which could put them at a disadvantage in a multimodal showdown. Hetzner’s latest cloud offerings, which start at around $40/month for a basic instance, might be a viable option for those looking to run a smaller model. However, for a 120B model, you’re looking at significantly more - likely in the range of $500-1000/month, depending on the specifics of your setup. DigitalOcean, on the other hand, offers more flexible pricing, but their highest-end instances still top out at 128GB of RAM, which might not be enough for a model of this size.

Conclusion-ish #

I’m not going to pretend like I have all the answers here. The fact is, a 120B dense multimodal Gemma model would be a monumental undertaking, requiring significant resources and expertise to pull off. But if anyone can do it, it’s Google. As u/LocalLLaMA_Enthusiast pointed out, “the real question is, can they make it accessible to the average user?” I’m skeptical, but I’m also excited to see what they come up with.

FAQs #

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